WO2020096172A1 - Dispositif électronique de traitement d'énoncé d'utilisateur et son procédé de commande - Google Patents

Dispositif électronique de traitement d'énoncé d'utilisateur et son procédé de commande Download PDF

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Publication number
WO2020096172A1
WO2020096172A1 PCT/KR2019/009716 KR2019009716W WO2020096172A1 WO 2020096172 A1 WO2020096172 A1 WO 2020096172A1 KR 2019009716 W KR2019009716 W KR 2019009716W WO 2020096172 A1 WO2020096172 A1 WO 2020096172A1
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WO
WIPO (PCT)
Prior art keywords
user
nlu
processor
user terminal
information
Prior art date
Application number
PCT/KR2019/009716
Other languages
English (en)
Inventor
Sean Minsung Kim
Jaeyung Yeo
Original Assignee
Samsung Electronics Co., Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Samsung Electronics Co., Ltd. filed Critical Samsung Electronics Co., Ltd.
Priority to EP19882344.5A priority Critical patent/EP3850620A4/fr
Priority to CN201980072576.XA priority patent/CN112970059B/zh
Publication of WO2020096172A1 publication Critical patent/WO2020096172A1/fr

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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/04—Segmentation; Word boundary detection
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/1815—Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/28—Constructional details of speech recognition systems
    • G10L15/30—Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/1822—Parsing for meaning understanding
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/223—Execution procedure of a spoken command
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/226—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics
    • G10L2015/227—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics of the speaker; Human-factor methodology

Definitions

  • a user terminal may process only voice inputs of the limited number.
  • the user terminal may transmit another voice input other than the voice inputs of the limited number to an external server, may receive the response, and may process the received voice input.
  • the voice inputs of the limited number may be configured to be processed by the user terminal, as the voice that a user is expected to enter frequently. As such, the user terminal may increase the overall voice input processing speed. However, because the voice entered frequently for each user is different and the voice input entered frequently as time goes on is changed in spite of the same user, the overall voice input processing speed may not increase depending on a user.
  • a controlling method of a system for updating an NLU model may include receiving first information associated with a user from an external electronic device associated with a user account, selecting at least one of the plurality of NLU models, based on at least part of the first information, and transmitting the selected at least one NLU model to the external electronic device, using at least one communication interface such that the external electronic device uses the selected at least one NLU model for natural language processing.
  • the user terminal 100 may be a terminal device (or an electronic device) capable of connecting to Internet, and may be, for example, a mobile phone, a smartphone, a personal digital assistant (PDA), a notebook computer, TV, a white household appliance, a wearable device, a head mount display (HMD), or a smart speaker.
  • a terminal device or an electronic device capable of connecting to Internet
  • PDA personal digital assistant
  • TV TV
  • white household appliance a wearable device
  • HMD head mount display
  • smart speaker a smart speaker
  • the processor 160 may also execute the program stored in the memory 150 to perform the specified function.
  • the processor 160 may execute at least one of the client module 151 or the SDK 153 to perform the following actions for processing a voice input.
  • the processor 160 may control the actions of the plurality of apps 155 via the SDK 153.
  • the following actions described as the actions of the client module 151 or the SDK 153 may be the action by the execution of the processor 160.
  • the client module 151 may receive the plan corresponding to the received voice input.
  • the client module 151 may display the result of executing a plurality of actions of an app in the display 140 depending on the plan.
  • the client module 151 may sequentially display the execution result of a plurality of actions in a display.
  • the user terminal 100 may display only a part of results (e.g., the result of the last action) of executing a plurality of actions, on the display.
  • the client module 151 may receive a request for obtaining information necessary to calculate the result corresponding to a voice input, from the intelligent server 200.
  • the information necessary to calculate the result may be the state information of the user terminal 100.
  • the client module 151 may transmit the necessary information to the intelligent server 200 in response to the request.
  • the client module 151 may include a voice recognition module. According to an embodiment, the client module 151 may recognize a voice input to perform the limited function, via the voice recognition module. For example, the client module 151 may launch an intelligent app that processes a voice input for performing an organic action, via a specified input (e.g., wake up!).
  • the intelligent server 200 may receive the information associated with a user's voice input from the user terminal 100 over a communication network. According to an embodiment, the intelligent server 200 may change the data associated with the received voice input to text data. According to an embodiment, the intelligent server 200 may generate a plan for performing a task corresponding to a user voice input, based on the text data.
  • the intelligent server 200 may transmit the result calculated depending on the generated plan to the user terminal 100 or may transmit the generated plan to the user terminal 100.
  • the user terminal 100 may display the result calculated depending on the plan, on a display.
  • the user terminal 100 may display the result of executing the action according to the plan, on the display.
  • the planner module 225 may determine the relationship between the plurality of actions and the plurality of concepts stepwise (or hierarchically). For example, the planner module 225 may determine the execution sequence of the plurality of actions, which are determined based on a user's intent, based on the plurality of concepts. In other words, the planner module 225 may determine the execution sequence of the plurality of actions, based on the parameters necessary to perform the plurality of actions and the result output by the execution of the plurality of actions. As such, the planner module 225 may generate a plan including information (e.g., ontology) of the relationship between a plurality of actions and a plurality of concepts. The planner module 225 may generate the plan, using the information stored in the capsule DB 230 storing a set of relationships between concepts and actions.
  • information e.g., ontology
  • the service server 300 may provide the user terminal 100 with a specified service (e.g., food order or hotel reservation).
  • the service server 300 may be a server operated by the third party.
  • the service server 300 may include a first service server 301, a second service server 302, and a third service server 305, which are operated by different third parties.
  • the service server 300 may provide the intelligent server 200 with information for generating a plan corresponding to the received voice input.
  • the provided information may be stored in the capsule DB 230.
  • the service server 300 may provide the intelligent server 200 with result information according to the plan.
  • FIG. 2 is a diagram illustrating the form in which relationship information between a concept and an action is stored in a database, according to various embodiments.
  • FIG. 3 is a view illustrating a screen in which a user terminal processes a received voice input through an intelligent app, according to various embodiments.
  • the first ASR module 221 may generate text data corresponding to the received voice input.
  • the first NLU module 223 may determine the user's intent and a parameter, using the text data.
  • the first planner module 225 may generate the plan corresponding to the received voice input.
  • the plan may be determined based on the determined intent and the determined parameter.
  • the intelligent server 200 may calculate the result using the generated plan and may transmit the calculated result to the user terminal 100. Furthermore, the intelligent server 200 may directly transmit the generated plan to the user terminal 100.
  • the user terminal 100 may sequentially perform specified actions based on the plan.
  • the NLU management module 290 may manage the second NLU module 173 of the user terminal 100.
  • the NLU management module 290 may manage an NLU module (e.g., the second NLU module 173) of at least one electronic device.
  • the user data manager module 295 may store the information associated with the user received from the user terminal 100, in the user history DB 296.
  • the information associated with the user may include at least one of the user's voice input, information of the user terminal 100, or the user's preference information.
  • the user terminal 100 may be a device that is logged in with a user account.
  • the information of the user terminal 100 may include information (e.g., identification information or setting information) of the logged-in user.
  • the user data manager module 295 may store the information of the user terminal 100 in the user history DB 296.
  • the user data manager module 295 may store processed information of the received voice input, in the user history DB 296.
  • the user data manager module 295 may analyze information associated with the user stored in the user history DB 296. For example, the user data manager module 295 may identify the intent processed by the user terminal 100 by analyzing the user log. For example, as illustrated in Table 1, the user log may include identification information of a plan, information about the name of an app, information about a user utterance, or the like. The user data manager module 295 may determine the recognized intent, using the identification information of a plan included in log information.
  • the intelligent server 200 may train the criterion for extracting the intent, using artificial intelligence (AI).
  • AI artificial intelligence
  • the criterion for extracting the intent in the user terminal 100 may be updated through machine learning.
  • the intelligent server 200 may generate a natural language recognition model for recognizing the intent included in the determined range.
  • the intelligent server 200 may implement the personalized voice processing system by changing (or updating) the intent recognized by the user terminal 100 using user data.
  • the user terminal 100 may receive a user input to select at least one intent through the intent list 621.
  • the user terminal 100 may display the selected intent in the intent list 621.
  • the user terminal 100 may display the selected app (e.g., "order americano”, “add whipping cream”, and “make a payment with Samsung Pay") through indicators 621a_1, 621a_2, and 621a_3.
  • the display device 860 may visually provide information to the outside (e.g., a user) of the electronic device 801.
  • the display device 860 may include, for example, a display, a hologram device, or a projector and control circuitry to control a corresponding one of the display, hologram device, and projector.
  • the display device 860 may include touch circuitry adapted to detect a touch, or sensor circuitry (e.g., a pressure sensor) adapted to measure the intensity of force incurred by the touch.
  • a connecting terminal 878 may include a connector via which the electronic device 801 may be physically connected with the external electronic device (e.g., the electronic device 802).
  • the connecting terminal 878 may include, for example, a HDMI connector, a USB connector, a SD card connector, or an audio connector (e.g., a headphone connector).
  • the wireless communication module 892 may identify and authenticate the electronic device 801 in a communication network, such as the first network 898 or the second network 899, using subscriber information (e.g., international mobile subscriber identity (IMSI)) stored in the subscriber identification module 896.
  • subscriber information e.g., international mobile subscriber identity (IMSI)
  • the instructions may cause the processor to select at least one of the plurality of NLU models when the number of times that the specified voice input of the user is received is not less than a specified value during a specified period.
  • the instructions may cause the processor to determine an intent corresponding to the voice input and to select the at least one NLU model based on at least one voice input corresponding to the specified intent when determining a specified intent more than a specified count.
  • the instructions may cause the processor to select the at least one NLU model corresponding to an intent for performing a function of a specified application program.
  • the selecting of the at least one of the plurality of NLU models may include determining an intent corresponding to the voice input and selecting the at least one NLU model based on at least one voice input corresponding to the specified intent when determining a specified intent more than a specified count.

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • User Interface Of Digital Computer (AREA)

Abstract

L'invention concerne un système comprenant au moins une interface de communication, au moins un processeur connecté fonctionnellement à ladite interface de communication, et au moins une mémoire connectée fonctionnellement audit processeur et maintenant une pluralité de modèles de compréhension de langage naturel (NLU). Ladite mémoire maintient des instructions qui, lorsqu'elles sont exécutées, amènent le processeur à recevoir des premières informations associées à un utilisateur en provenance d'un dispositif électronique externe associé à un compte d'utilisateur, à l'aide de ladite interface de communication, à sélectionner au moins un modèle parmi la pluralité de modèles de NLU, sur la base d'au moins une partie des premières informations, et à transmettre ledit modèle de NLU sélectionné au dispositif électronique externe, à l'aide de ladite interface de communication de sorte que le dispositif électronique externe fasse appel audit modèle de NLU sélectionné pour un traitement de langage naturel.
PCT/KR2019/009716 2018-11-07 2019-08-05 Dispositif électronique de traitement d'énoncé d'utilisateur et son procédé de commande WO2020096172A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP19882344.5A EP3850620A4 (fr) 2018-11-07 2019-08-05 Dispositif électronique de traitement d'énoncé d'utilisateur et son procédé de commande
CN201980072576.XA CN112970059B (zh) 2018-11-07 2019-08-05 用于处理用户话语的电子装置及其控制方法

Applications Claiming Priority (2)

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KR10-2018-0135771 2018-11-07
KR1020180135771A KR20200052612A (ko) 2018-11-07 2018-11-07 사용자 발화를 처리하는 전자 장치, 및 그 전자 장치의 제어 방법

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US (2) US10699704B2 (fr)
EP (1) EP3850620A4 (fr)
KR (1) KR20200052612A (fr)
CN (1) CN112970059B (fr)
WO (1) WO2020096172A1 (fr)

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US11538470B2 (en) 2022-12-27
EP3850620A1 (fr) 2021-07-21
US20200143798A1 (en) 2020-05-07
EP3850620A4 (fr) 2021-12-08
CN112970059B (zh) 2023-11-24
CN112970059A (zh) 2021-06-15
US10699704B2 (en) 2020-06-30
KR20200052612A (ko) 2020-05-15
US20200335094A1 (en) 2020-10-22

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